1. Neighborhood - based Tag Prediction
Adriana Budura
joint work with:
Sebastian Michel, Philippe Cudré-Mauroux, Karl Aberer 1
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

2. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Outline
Motivation
Principles of Tag Propagation
Scoring Model
Top-k Tag Inference
Experimental Results
Conclusions 2
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

3. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Motivation
Tagging portals « Web 2.0 »
users attach keywords (tags) to resources:
flickr, del.icio.us, citeulike,…
Tags:
unstructured textual information
reflect the meaning of resources for users  powerful tool to improve search
BUT: we need many tags and users are lazy 
Therefore…. Automatic Tag Inference 3
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

4. Neighborhood -based Tag Prediction
IDEA: copy tags from other resources
Semantically related resources –> related tags
How to discover semantically similar resources?
Resources are connected via links (e.g., HTML, citations )
neighborhood of a resource captures its context
(e.g., citations in „Related Work“ )
propagate tags along the edges of the graph
How relevant is a tag found in the neighborhood?
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

5. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Computational Model
3 concepts:
Documents
the resources for which we infer tags; uniquely identifiable
in our scenario: scientific publications, Web pages
Tags
keywords attached to the resources
Document neighborhoods
documents connected by users  graph
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

6. How relevant is a tag found in the neighborhood?
Neighborhood defines context (far away -> less related)
Enough support in the neighborhood
Some tags are more likely to occur together
Similar documents are likely to share the same tags
Tag Distance
Tag Occurence
Tag Co-Occurence
Document – Document Similarity
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

7. Principles of Tag Propagation e.g. Citation graph of publications
d_init
Tag Occurence
Doc-Doc Similarity
TopK
distributed IR IR
ranking
PageRank
P2P
Tag Co-Occurence
Tag Distance IR
distributed
P2P
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

8. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Overview
Motivation
Principles of Tag Propagation
Scoring Model
Top-k Tag Inference
Experimental Results
Conclusions 8
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

9. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
(1) Tag Co-Occurrence
relevance of a tag t for d_init based on the tags already assigned to d_init ?
conditional probability:
d_init can have more than one initial tag
=> we aggregate for sets of tags T(d_init) 9
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

10. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
(2) Doc-Doc Similarity
relevance of a tag t (coming from a document d) for d_init, based on the similarity between d and d_init ?
vector space model:
for documents that are several hops away we aggregate 10
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

11. (3) Tag Distance / (4) Tag Occurence
the distance between the documents d_init and d with tag t
 smallest path
Tag Occurrence
what is the popularity (support) of a tag in the neighborhood
expressed as a sum over all scores for a tag t 11
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

12. Putting it All Together
Combined Scoring Function:
- sum of partial scores for each occurrence of a tag t in the neighborhood d_init
Tag Occurence
Doc-Doc Similarity,
Tag Distance
Tag Co-Occurence 12
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

13. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Overview
Motivation
Principles of Tag Propagation
Scoring Model
Top-k Tag Inference
Experimental Results
Conclusions 13
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

14. Inferring tags for a document
traverse the graph of documents and gather tags for the initial document
do not visit the whole neighborhood
 need smart graph traversal
the scoring model can compute a score for “every” tag
 top-k tags are enough
… when should we stop?
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

15. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Graph Traversal
Precomputed: Tags + Scores for each document
Doc 1
Doc 1
Doc 2
P2P, 0.3
Tag, 0.28
Social, 0.25
Paper, 0.2
2009, 0.1
Social, 0.4
Search, 0.33
Budura, 0.25
Tag, 0.2
Paper, 0.2
Doc 2
D_init
Select the next document based on the doc-doc similarity 15
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

16. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Top-K Graph Traversal
List of all neighbors sorted by doc-doc sim
Select best document
Doc x
Visited
Doc x
P2P, 0.3
Tag, 0.28
Social, 0.25
Paper, 0.2
2009, 0.1
Social, 0.4
Search, 0.33
Budura, 0.25
Tag, 0.2
Paper, 0.2
D_init
Social, 0.65
Paper, 0.4
Tag, 0.48
P2P, 0.3
....
top-k 16
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

17. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Top-k Tag Inference
Fagin et al. - NRA Algorithm w b
for each candidate tag
worst_score = actual score
best_score = worst_score +
best_to_come_score
prune a tag when
best_score < score of tag currently at rank k
stop when
seen k tags && no candidate tags left w b w b
score
(m-m‘) *
Top-k, pos. k
Candidate
Expelled
unknown final “score” mass for each tag
Consider ONLY m occurences for each tag
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

18. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Overview
Motivation
Principles of Tag Propagation
Scoring Model
Top-k Tag Inference
Experimental Results
Conclusions 18
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

19. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Experimental Setup
Datasets
del.icio.us (120K bookmarks)
CiteULike/CiteSeer (2200 crawled pdfs)
Measures of Interest:
Precision (user study)
Relative precision (computed based on already assigned tags)
Cost (number of visited neighbors) 19
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

20. Experimental Results: CiteULike
30 initial documents
manual precision evaluation (user study) m k
Precision
Neighbors 3
0.73 41 5
0.65 93 7
0.55 74
0.7
124
153
0.57
247
0.72
243
257
356 20
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

21. Experimental Results: Del.icio.us
120 initial documents
relative precision evaluation m k
Precision
Neighbors 3
0.5
1.65 5
0.42 7
0.36
1.67 21
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09

22. Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09
Conclusions
Tag inference over edges of resource graphs
4 principles of tag propagation
Scoring model
Top-k tag inference with modest access to the resource graph 22
Adriana Budura “Neighborhood – based Tag Prediction” - ESWC’09